Abb Research Ltd.

Konrad Stadler

Thomas Von Hoff

Andreas Poncet

Eduardo Gallestey Alvarez

Sebastian Gaulocher

Alvarez Eduardo Gallestey

Jan Poland

Hamed Foroush

Hamed Shisheh Foroush

John Brooks

Ross Taylor

A system and method are disclosed for observing a change of mass inside a grinding unit as a part of a grinding process with a storing unit. The change of mass is derived from a mass balance for the grinding unit and a mass balance for the storing unit.

Method of observing a change of mass inside a grinding unit

A model-based control of an industrial process using a merged MLD system model is provided for the estimation and subsequent control of the process. An optimization of an objective function is performed. The objective function includes a difference between an observed quantity and an output variable of a Mixed Logical Dynamic (MLD) system model of the process. The optimization is performed as a function of state variables of the MLD system model, over a number of time steps in the past, and subject to constraints defined by the MLD system model's dynamics. The optimizing values of the state variables are retained as estimated initial states for subsequent control of the process in a model-based manner including the same MLD system model. The single MLD system model is a combination or merger of individual MLD subsystem models representing the sub-processes of the process, and may be elaborated during a customization step.

Estimating initial states of a system model for controlling an industrial process

For monitoring (unmeasured) process states of a rotating machine having a combustion chamber (e.g. a gas turbine), compositions of educts entering the combustion chamber are measured. Based on the compositions of the educts, the composition of the product produced by the combustion chamber can be determined. Moreover, the mechanical power (P) generated by the rotating machine can be determined. Based on the mechanical power (P), the composition of the educts and product, and stoichiometric relationships of educts and product, the values of process states, such as the air mass flow (w) through the compressor leading into the combustion chamber and/or the gas mass flow (w), the composition and/or the temperature (T) of exhaust gas exiting the combustion chamber can be determined. Based on precise measurements of the educt (e.g. the composition of air and fuel in the combustion process), the product (i.e. the composition of the exhaust gas and its temperature) is derived and, without the need of an iterative or recursive method, the turbine inlet temperature (T) can be monitored and controlled.

Method and system for monitoring process states of an internal combustion engine

The present invention is concerned with the estimation of health parameters p(k) representing symptoms of a slowly degrading system, in particular industrial gas turbines. According to the invention, an estimation of a true health or independent parameter vector at time step k uses the estimation of the true health or parameter vector at a previous time step k&#x2212;1 as a starting value for the production of a predicted health parameter vector at time step k. Based on the latter and a set of measured values of input variables of an extended model of the system, a prediction of output variables of the model is produced. This predicted model output is compared with measured values of the output variables to yield an error. From this error, a health parameter estimator in turn produces a health parameter estimate as a revision of the predicted health parameters. Preferably, the estimation of a true health or independent parameter vector at time step k includes a generalised process noise representing stochastic uncertainty and/or a-priori knowledge of an evolution of the health parameters.

Estimating health parameters or symptoms of a degrading system

The present disclosure is concerned with the identification of fault origins of slowly degrading systems such as industrial gas turbines. Following the initial input of some parameter values, exemplary procedure comprises a number of steps which are repeated periodically during the operation of the gas turbine. First, for each potential fault such as e.g. a contamination of compressor blades, erosion of turbine blades or corrosion of machine parts, a pre-symptom fault probability, characteristic of the specific gas turbine considered, and a symptom-conditional fault probability are calculated. In a second step, on-line data from the monitoring devices are loaded in order to update information about symptoms such as e.g. reductions in polytropic efficiency and in flow capacity, changes in vibration spectrum, or other machine condition indicators, and a post-symptom fault probability is calculated. In a third step, the calculated fault probabilities are displayed and transmitted to a planning system for scheduling of gas turbine operation or maintenance actions.

Monitoring a degrading system

The present invention is concerned with the estimation of health parameters p(k) representing symptoms of a slowly degrading system, in particular industrial gas turbines. According to the invention, an estimation of a true health or independent parameter vector at time step k uses the estimation of the true health or parameter vector at a previous time step k&#x2212;1 as a starting value for the production of a predicted health parameter vector at time step k. Based on the latter and a set of measured values of input variables of an extended model of the system, a prediction of output variables of the model is produced. This predicted model output is compared with measured values of the output variables to yield an error. From this error, a health parameter estimator in turn produces a health parameter estimate as a revision of the predicted health parameters. Preferably, the estimation of a true health or independent parameter vector at time step k includes a generalized process noise representing stochastic uncertainty and/or a-priori knowledge of an evolution of the health parameters.

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Niederweningen, Switzerland

Konrad Stadler